Few-shot learning has gained a lot of attention in image classification
 [1]}, [2]}, [3]}, [4]}, [5]}.
Prototypical networks [6]} use the mean of embedded
support examples to represent novel class prototypes and classifies query
examples by comparing their distances to the class prototypes. More recently,
Qi  [7]} propose a weight imprinting process to learn the prototypes
on the unit hypersphere. Learning on the unit hypersphere has been employed by
other few-shot learning algorithms for better generalization [8]}
and stabilizing the training [9]}.
Most recently, Yang  [10]} propose to make
the distributions more Gaussian like
by transforming the features of the support set and query set using Tukey’s Ladder of
Powers transformation [11]}. It is shown that Tukey's normalization significantly improves
the performance of few-shot prototypical learning.
As the scope of these methods is limited to supervised learning,
we compare different normalization and transformations used in the literature
for the weakly-supervised task of COL in sec:ablation.
